Sparse fast Clifford Fourier transform

被引：0
|
作者：
Rui Wang
Yi-xuan Zhou
Yan-liang Jin
Wen-ming Cao
机构：
[1] Shanghai University,School of Communication and Information Engineering
[2] Shenzhen University,College of Information Engineering
[3] University of Missouri,Department of Electrical and Computer Engineering
来源：
Frontiers of Information Technology & Electronic Engineering | 2017年 / 18卷
关键词：
Sparse fast Fourier transform (sFFT); Clifford Fourier transform (CFT); Sparse fast Clifford Fourier transform (SFCFT); Clifford algebra; TP391;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The Clifford Fourier transform (CFT) can be applied to both vector and scalar fields. However, due to problems with big data, CFT is not efficient, because the algorithm is calculated in each semaphore. The sparse fast Fourier transform (sFFT) theory deals with the big data problem by using input data selectively. This has inspired us to create a new algorithm called sparse fast CFT (SFCFT), which can greatly improve the computing performance in scalar and vector fields. The experiments are implemented using the scalar field and grayscale and color images, and the results are compared with those using FFT, CFT, and sFFT. The results demonstrate that SFCFT can effectively improve the performance of multivector signal processing.
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页码：1131 / 1141
页数：10
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